Sharif Digital Repository

In this paper, we investigate the recovery of a sparse weight vector (parameters vector) from a set of noisy linear combinations. However, only partial information about the matrix representing the linear combinations is available. Assuming a low-rank structure for the matrix, one natural solution would be to first apply a matrix completion to the data, and then to solve the resulting compressed sensing problem. In big data applications such as massive MIMO and medical data, the matrix completion step imposes a huge computational burden. Here, we propose to reduce the computational cost of the completion task by ignoring the columns corresponding to zero elements in the sparse vector. To... 

The fusion of the optical and inertial tracking systems seems an attractive solution to solve the shadowing problem of the optical tracking systems, and remove the time integration troubles of the inertial sensors. We developed a fusion algorithm for this purpose, based on the Kalman filter, and examined its efficacy to improve the position and orientation data, obtained by each individual system. Experimental results indicated that the proposed fusion algorithm could effectively estimate the 2 seconds missing data of the optical tracker  

In this paper, we will investigate the efficacy of IMAT (Iterative Method of Adaptive Thresholding) in recovering the sparse signal (parameters) for linear models with random missing data. Sparse recovery rises in compressed sensing and machine learning problems and has various applications necessitating viable reconstruction methods specifically when we work with big data. This paper will mainly focus on comparing the power of Iterative Method of Adaptive Thresholding (IMAT) in reconstruction of the desired sparse signal with that of LASSO. Additionally, we will assume the model has random missing information. Missing data has been recently of interest in big data and machine learning... 

Big-data server applications frequently encounter data misses, and hence, lose significant performance potential. One way to reduce the number of data misses or their effect is data prefetching. As data accesses have high temporal correlations, temporal prefetching techniques are promising for them. While state-of-the-art temporal prefetching techniques are effective at reducing the number of data misses, we observe that there is a significant gap between what they offer and the opportunity. This work aims to improve the effectiveness of temporal prefetching techniques. We identify the lookup mechanism of existing temporal prefetchers responsible for the large gap between what they offer and... 

Purpose: Robot localization in dynamic, cluttered environments is a challenging problem because it is impractical to have enough knowledge to be able to accurately model the robot’s environment in such a manner. This study aims to develop a novel probabilistic method equipped with function approximation techniques which is able to appropriately model the data distribution in Markov localization by using the maximum statistical power, thereby making a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environments. Design/methodology/approach: The parameter vector of the statistical model is in the form of positions of easily detectable artificial landmarks... 

Despite complex fluctuations, missing data, and maintenance costs of detectors, traffic volume forecasting at intersections is still a challenge. Moreover, most existing forecasting methods consider an isolated intersection instead of multiple adjacent ones. By accurately forecasting the volume of short-term traffic, a low-cost method can be provided to solve the problems of congestion, delay, and breakdown of detectors in the road transport system. This paper outlines a novel hybrid method based on deep learning to estimate short-term traffic volume at three adjacent intersections. The gated recurrent unit (GRU) and long short-term memory (LSTM) bilayer network with wavelet transform (WL)...